Recognition of partially occluded shapes using a neural optimization network
The current work presents an algorithm for recognition of partially occluded shapes in a cluttered scene. The images are represented by a sequence of angles subtended at the corner points. The cost due to comparison between the input cluttered scene and the stored images is obtained from a cost function designed to store the obtained information in the form of a cost matrix which is presented to the input of an optimization network. The parameters of the optimization network are determined so as to minimize an energy function, the minima of which occur at the solutions of the problem. The results, as obtained in different domains (2D shapes and projected 3D shapes) with different degrees of occlusion, provide interesting insights into the operation of the algorithm as well as avenues for future research.
Machine Graphics and Vision
Banerjee, B. (2005). Recognition of partially occluded shapes using a neural optimization network. Machine Graphics and Vision (1-2), 3-23. Retrieved from https://digitalcommons.memphis.edu/facpubs/14156